Improving prediction of students’ performance in intelligent tutoring systems using attribute selection and ensembles of different multimodal data sources
Autor
Chango, Wilson
Cerezo, Rebeca
Sánchez‑Santillán, Miguel
Azevedo, Roger
Romero Morales, C.
Editor
SpringerFecha
2021Materia
Predicting academic performanceIntelligent tutoring systems
Multisource data
Multimodal learning
Data fusion
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The aim of this study was to predict university students’ learning performance using different sources of performance and multimodal data from an Intelligent Tutoring System. We collected and preprocessed data from 40 students from different multimodal sources: learning strategies from system logs, emotions from videos of facial expressions, allocation and fixations of attention from eye tracking, and performance on posttests of domain knowledge. Our objective was to test whether the prediction could be improved by using attribute selection and classification ensembles. We carried out three experiments by applying six classification algorithms to numerical and discretized preprocessed multimodal data. The results show that the best predictions were produced using ensembles and selecting the best attributes approach with numerical data.